Algo's Integrated Knowledge Translation Process in Homecare Services: A Cross-Sectional Correlational Study for Identifying its Level of Utilization and its Associated Characteristics
Bibliographic record
Abstract
Background. Algo is an integrated knowledge translation (IKT)-based algorithm for supporting occupational therapists (OTs) with skill mix for selecting bathing equipment. While IKT approaches are increasingly valued in implementation science, their benefits with respect to the utilization of knowledge in clinical settings are scarcely documented. Purpose. To identify Algo's level of utilization and the characteristics associated with its level of utilization. Method. A cross-sectional correlational study was conducted with OTs working in homecare services (HCS) through an online survey based on Knott and Wildavsky's classification and the Promoting Action on Research Implementation in Health Services ( PARIHS ) framework. Findings. Almost half (48%) of the OTs surveyed (n = 125; participation rate: 16%) reached one of the seven levels of utilization. While Evidence characteristics are perceived as facilitators to its utilization, Context statements indicate an unfavorable organizational climate to the implementation of change. Implications. Strategies should target additional stakeholders (e.g., HCS managers) and organizational adjustments in HCS to sustain Algo's utilization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".